Abstract

Many processes important to chemistry, materials science, and biology cannot be described without considering electronic and nuclear-level dynamics and their coupling to slower, cooperative motions of the system. These inherently multiscale problems require computationally efficient and accurate methods to converge statistical properties. In this paper, a method is presented that uses data directly from condensed phase ab initio simulations to develop reactive molecular dynamicsmodels that do not require predefined empirical functions. Instead, the interactions used in the reactive model are expressed as linear combinations of interpolating functions that are optimized by using a linear least-squares algorithm. One notable benefit of the procedure outlined here is the capability to minimize the number of parameters requiring nonlinear optimization. The method presented can be generally applied to multiscale problems and is demonstrated by generating reactive models for the hydrated excess proton and hydroxide ion based directly on condensed phase ab initiomolecular dynamics simulations. The resulting models faithfully reproduce the water-ion structural properties and diffusion constants from the ab initio simulations. Additionally, the free energy profiles for proton transfer, which is sensitive to the structural diffusion of both ions in water, are reproduced. The high fidelity of these models to ab initio simulations will permit accurate modeling of general chemical reactions in condensed phase systems with computational efficiency orders of magnitudes greater than currently possible with ab initio simulation methods, thus facilitating a proper statistical sampling of the coupling to slow, large-scale motions of the system.

Received 31 May 2012Accepted 25 July 2012Published online 15 August 2012

Acknowledgments:

This research was supported in part by the National Science Foundation (NSF, Grant No. CHE-1214087) and the National Institutes of Health (NIH, Grant No. R01-GM053148). We also acknowledge support from the Department of Defense Multidisciplinary University Research Initiative through the (U.S.) Army Research Office (USARO Grant No. W911NF-10-1-0520), the (U.S.) Department of Energy (DOE) under Contract No. DE-AC02-06CH11357, and an Argonne National Laboratory (ANL) Computational Science Postdoctoral Fellowship to C.K. The computations in this work were supported in part by a grant of computer time from the U.S. Department of Defense (DOD) High Performance Computing Modernization Program at the Navy, Engineer Research and Development Center (ERDC), and Air Force Research Laboratory (AFRL) DOD Supercomputing Resource Centers. These computations were also supported in part by the National Science Foundation Teragrid and Extreme Science and Engineering Discovery Environment (XSEDE) computing resources provided by the Texas Advanced Computing Center under Grant No. MCA94P017.